TY - GEN
T1 - Performance prediction and evaluation of parallel applications in KVM, Xen, and VMware
AU - Hong, Cheol Ho
AU - Kim, Beom Joon
AU - Kim, Young Pil
AU - Park, Hyunchan
AU - Yoo, Hyuck
PY - 2014
Y1 - 2014
N2 - Cloud computing platforms are considerably attractive for parallel applications that perform large-scale, computationally intensive tasks. These platforms can provide elastic computing resources to the parallel software owing to system virtualization technology. Almost every cloud service provider operates on a pay-per-use basis, and therefore, it is important to estimate the performance of parallel applications before deploying them. However, a comprehensive study that can predict the performance of parallel applications remains unexplored and is still a research topic. In this paper, we provide a theoretical performance model that can predict the performance of parallel applications in different virtual machine scheduling policies and evaluate the model in representative hypervisors including KVM, Xen, and VMware. Through this analysis and evaluation, we show that our performance prediction model is accurate and reliable.
AB - Cloud computing platforms are considerably attractive for parallel applications that perform large-scale, computationally intensive tasks. These platforms can provide elastic computing resources to the parallel software owing to system virtualization technology. Almost every cloud service provider operates on a pay-per-use basis, and therefore, it is important to estimate the performance of parallel applications before deploying them. However, a comprehensive study that can predict the performance of parallel applications remains unexplored and is still a research topic. In this paper, we provide a theoretical performance model that can predict the performance of parallel applications in different virtual machine scheduling policies and evaluate the model in representative hypervisors including KVM, Xen, and VMware. Through this analysis and evaluation, we show that our performance prediction model is accurate and reliable.
UR - http://www.scopus.com/inward/record.url?scp=84906343035&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-09873-9-9
DO - 10.1007/978-3-319-09873-9-9
M3 - Conference contribution
SN - 9783319098722
VL - 8632 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 99
EP - 110
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
T2 - 20th International Conference on Parallel Processing, Euro-Par 2014
Y2 - 25 August 2014 through 29 August 2014
ER -